CN116187562A - Electric vehicle charging capacity demand prediction method and related device - Google Patents

Electric vehicle charging capacity demand prediction method and related device Download PDF

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CN116187562A
CN116187562A CN202310170649.7A CN202310170649A CN116187562A CN 116187562 A CN116187562 A CN 116187562A CN 202310170649 A CN202310170649 A CN 202310170649A CN 116187562 A CN116187562 A CN 116187562A
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李思纤
陆敏怡
傅敏杰
张天尘
李天宇
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State Grid Shanghai Electric Power Co Ltd
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Abstract

The electric vehicle charging capacity demand prediction method and the related device comprise the following steps: collecting charging power data aggregated by the electric automobiles in the cells and attribute data of the cells; preprocessing charging power data and cell attribute data to obtain data which are grouped according to a time sequence; dividing the preprocessed data into a training set, a verification set and a test set; and constructing a GRU neural network model, training the model, and predicting the trained model by using a test data set. The invention is based on deep learning prediction of the cyclic neural network and the GRU cyclic gate unit, can improve the accuracy of electric vehicle charging capacity demand prediction, effectively predicts short-term, medium-term and long-term charging capacity demands, and supports planning operation management decision of the power distribution network.

Description

Electric vehicle charging capacity demand prediction method and related device
Technical Field
The invention relates to the field of electric vehicle charging demand prediction, in particular to an electric vehicle charging capacity demand prediction method and a related device.
Background
With the rapid popularization of electric vehicles, great impact is caused on power distribution systems of a plurality of communities. Most of the prior power distribution systems do not fully estimate and plan the situation, and the ultra-expected large-quantity access of the charging piles is likely to cause overload of power distribution capacity load, thereby causing tripping or burning out equipment. The method has the advantages that the charging capacity requirement of the electric car in the district needs to be predicted and pre-warned from a plurality of time dimensions, and measures are taken in time to prevent the problem. The prior art is based on stability analysis, predicts the monthly electricity consumption of a cell, or obtains historical sample data of the power consumption of the residential users of the cell, and predicts the severe name cell sub-term load data based on an Adaboost iterative algorithm. The cell electric automobile charging demand is directly related to factors such as cell scale, parking space quantity and the like, the prior art belongs to conventional electric power and electricity quantity prediction, the cell characteristics and the electric automobile charging behavior characteristics are not optimized, and the prediction accuracy and efficiency are difficult to meet the cell electric automobile charging demand prediction demand.
Disclosure of Invention
The invention aims to provide a method and a related device for predicting the charging capacity requirement of an electric automobile, which are used for solving the problems of low prediction accuracy and low efficiency caused by lack of consideration of factors such as cell scale, parking space quantity and the like in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in one aspect of the present invention, a method for predicting a charging capacity demand of an electric vehicle is provided, including:
collecting charging power data aggregated by the electric automobiles in the cells and attribute data of the cells;
preprocessing charging power data and cell attribute data to obtain data which are grouped according to a time sequence;
dividing the preprocessed data into a training set, a verification set and a test set;
and constructing a GRU neural network model, training the GRU neural network model, and predicting the trained GRU neural network model by using a test set.
Further, the cell attribute data includes: the method comprises the steps of number of cell households, number of cell fixed parking spaces, number of cell shared parking spaces, number of cell charging piles, cell category, cell room price, cell construction year and cell household historical electricity consumption distribution.
Further, the data after being grouped according to the time sequence is obtained by preprocessing the cell attribute data:
data were converted to hours, days, zhou Pindu and normalized with mean variance, respectively, as follows:
Figure BDA0004097959250000021
wherein: where x is the value to be normalized, x scale For the values after normalization, μ is the average value of the samples, and S is the standard deviation of the samples.
Further, the training set, the verification set and the test set are specifically divided into:
6：2：2。
further, the constructing a GRU neural network model includes:
z t ＝σ(W z ·[h t-1 ，x t ])
r t ＝σ(W r ·[h t-1 ，x t ])
Figure BDA0004097959250000022
Figure BDA0004097959250000023
wherein z is t A representative update gate for deciding information to be discarded and new information to be added; r is (r) t Representing a reset gate, a reset gateFor deciding a degree to which previous information is discarded; h is a t Representing candidate memory, h t-1 Representing the candidate memory of the previous moment in time,
Figure BDA0004097959250000024
representing a memory gate, W z Representing the parameters of the calculation update gate, W r Representing the parameters of the calculation reset gate, tanh is a hyperbolic tangent function, W represents the parameters of the calculation memory gate, sigma represents a sigmoid function, x t Representing the input data.
Further, training the GRU neural network model specifically includes:
the GRU is a time sequence model, uses average absolute error as a loss function, namely, the average of absolute values of deviation of arithmetic average values of each single predicted value and all predicted values, and uses mean square logarithmic error as a monitoring index of a network, namely, the square of the average value of the logarithmic loss function, to carry out overall training.
Further, the test data set is used for predicting in the trained GRU neural network model by using the test data set, and the prediction results of hours, days and Zhou Pindu are obtained respectively, namely the charging capacity of the electric car in the district in a future period of time.
In another aspect of the present invention, there is provided a system for predicting a charge capacity demand of an electric vehicle, including:
the collecting module is used for collecting the charging power data after the aggregation of the electric automobiles in the cells and the attribute data of the cells;
the preprocessing module is used for preprocessing the charging power data and the cell attribute data to obtain data which are grouped according to a time sequence;
the data dividing module is used for dividing the preprocessed data into a training set, a verification set and a test set;
and the training prediction module is used for constructing a GRU neural network model, training the model and predicting the trained model by using the test data set.
In yet another aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method for predicting the charge capacity demand of an electric vehicle when executing the computer program.
In yet another aspect of the present invention, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for predicting charge capacity demand of an electric vehicle is provided.
Compared with the prior art, the invention has the following technical effects:
according to the invention, based on the cyclic neural network and the GRU cyclic gate unit, the deep learning prediction is performed by combining the charging power data and the cell attribute data of the electric automobile after aggregation of the cells, so that the accuracy of the electric automobile charging capacity demand prediction is improved, and compared with the traditional machine learning model and the traditional cyclic neural network model, the prediction accuracy is higher and the error is smaller.
Furthermore, the method converts the acquired data into hours, days and Zhou Pindu, adopts standardized processing of mean variance normalization, corresponds to short-term, medium-term and long-term prediction, effectively predicts short-term, medium-term and long-term charging capacity requirements, and supports planning operation management decisions of the power distribution network.
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FIG. 1 is a flow chart of the present invention.
Fig. 2 is a general flow chart of the present invention.
FIG. 3 is a schematic diagram of the system of the present invention.
FIG. 4 is a schematic diagram of a GRU neural network model according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
referring to fig. 1, the present invention provides an embodiment of a method for predicting a charging capacity requirement of an electric vehicle, which includes the following steps:
the electric vehicle charging capacity demand prediction method comprises the following steps:
collecting charging power data aggregated by the electric automobiles in the cells and attribute data of the cells;
preprocessing charging power data and cell attribute data to obtain data which are grouped according to a time sequence;
dividing the preprocessed data into a training set, a verification set and a test set;
and constructing a GRU neural network model, training the GRU neural network model, and predicting the trained GRU neural network model by using a test set.
According to the invention, based on the cyclic neural network and the GRU cyclic gate unit, the deep learning prediction is performed by combining the charging power data and the cell attribute data of the electric automobile after aggregation of the cells, so that the accuracy of the electric automobile charging capacity demand prediction is improved, and compared with the traditional machine learning model and the traditional cyclic neural network model, the prediction accuracy is higher and the error is smaller.
The present invention also provides an embodiment, as shown in fig. 2, specifically including:
1. and charging capacity data of the electric automobile in the residential area.
Collecting the aggregated charging power, the number of cell households, the number of cell fixed parking spaces, the number of cell shared parking spaces, the number of cell charging piles, the cell category, the cell room price, the cell construction year and the historical electricity consumption distribution of the cell households of the residential area;
2. and (5) preprocessing.
3. The preprocessed data is divided into a training set, a verification set and a test set.
The specific process of preprocessing the charging capacity data of the electric automobile in the residential area comprises the following steps:
2) Data were converted to hours, days, zhou Pindu and normalized separately: the mean variance normalization is adopted, and is shown as follows:
Figure BDA0004097959250000051
wherein: where x is the value to be normalized, x scale For the values after normalization, μ is the average value of the samples, and S is the standard deviation of the samples.
3) The data is time-series packetized.
4. The GRU neural network model is constructed, and the network structure is shown in fig. 4:
z t ＝σ(W z ·[h t-1 ，x t ])
r t ＝σ(W r ·[h t-1 ，x t ])
Figure BDA0004097959250000052
/>
Figure BDA0004097959250000053
in FIG. 4, the reference numeral x t As input, the GRU neural network model is reset and updated to output the representative candidate memory h t Wherein z is t A representative update gate for deciding information to be discarded and new information to be added; r is (r) t Representing a reset gate for determining the extent to which previous information was discarded; h is a t Representing candidate memory, h t-1 Representing the candidate memory of the previous moment in time,
Figure BDA0004097959250000054
representing a memory gate, W z Representing the parameters of the calculation update gate, W r Representing the parameters of the calculated reset gate, tanh is a hyperbolic tangent function, x t Representing the input data.
5. And using the average absolute error as a loss function, and using the mean square logarithmic error as a monitoring index of the network to carry out overall training.
6. And 4, predicting in the trained model in the step 4 by using the test data set to obtain the prediction results of hour, day and Zhou Pindu respectively.
Comparison of prediction results of different models:
Figure BDA0004097959250000061
wherein RMSE is root mean square error and MAPE is mean absolute percentage error. The table above describes the comparison of root mean square error and mean absolute percentage error under different prediction models.
In still another embodiment of the present invention, as shown in fig. 3, an electric vehicle charging capacity demand prediction system is provided, which can be used to implement the above electric vehicle charging capacity demand prediction method, and specifically the system includes:
the collecting module is used for collecting the charging power data after the aggregation of the electric automobiles in the cells and the attribute data of the cells;
the preprocessing module is used for preprocessing the charging power data and the cell attribute data to obtain data which are grouped according to a time sequence;
the data dividing module is used for dividing the preprocessed data into a training set, a verification set and a test set;
and the training prediction module is used for constructing a GRU neural network model, training the model and predicting the trained model by using the test data set.
The division of the modules in the embodiments of the present invention is schematically only one logic function division, and there may be another division manner in actual implementation, and in addition, each functional module in each embodiment of the present invention may be integrated in one processor, or may exist separately and physically, or two or more modules may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions within a computer storage medium to implement the corresponding method flow or corresponding functions; the processor provided by the embodiment of the invention can be used for the operation of the electric vehicle charging capacity demand prediction method.
In yet another embodiment of the present invention, a storage medium, specifically a computer readable storage medium (Memory), is a Memory device in a computer device, for storing a program and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for predicting electric vehicle charge capacity requirements in the above-described embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims (10)

1. The electric vehicle charging capacity demand prediction method is characterized by comprising the following steps:
collecting charging power data aggregated by the electric automobiles in the cells and attribute data of the cells;
preprocessing charging power data and cell attribute data to obtain data which are grouped according to a time sequence;
dividing the preprocessed data into a training set, a verification set and a test set;
and constructing a GRU neural network model, training the GRU neural network model, and predicting the trained GRU neural network model by using a test set.
2. The electric vehicle charge capacity demand prediction method according to claim 1, wherein the cell attribute data includes: the method comprises the steps of number of cell households, number of cell fixed parking spaces, number of cell shared parking spaces, number of cell charging piles, cell category, cell room price, cell construction year and cell household historical electricity consumption distribution.
3. The method for predicting the charging capacity requirement of an electric vehicle according to claim 1, wherein the method for preprocessing the cell attribute data to obtain the data grouped according to the time sequence specifically comprises:
the data are converted into hours, days and Zhou Pindu, and the standardized treatment of mean variance normalization is adopted respectively, and the specific method is as follows:
Figure FDA0004097959240000011
wherein: where x is the value to be normalized, x scale For the values after normalization, μ is the average value of the samples, and S is the standard deviation of the samples.
4. The method for predicting the charge capacity demand of an electric vehicle according to claim 1, wherein the specific dividing ratio of the training set, the validation set and the test set is 6:2:2.
5. The method of claim 1, wherein the constructing a GRU neural network model comprises:
z t ＝σ(W z ·[h t-1 ，x t ])
r t ＝σ(W r ·[h t-1 ，x t ])
Figure FDA0004097959240000012
Figure FDA0004097959240000013
wherein z is t A representative update gate for deciding information to be discarded and new information to be added; r is (r) t Representing a reset gate for determining the extent to which previous information was discarded; h is a t Representing candidate memory, h t-1 Representing the candidate memory of the previous moment in time,
Figure FDA0004097959240000021
representing a memory gate, W z Representing the parameters of the calculation update gate, W r Representing the parameters of the calculation reset gate, tanh is a hyperbolic tangent function, W represents the parameters of the calculation memory gate, sigma represents a sigmoid function, x t Representing the input data.
6. The method for predicting the charge capacity requirement of an electric vehicle according to claim 1, wherein the training of the GRU neural network model is specifically as follows:
the overall training is performed using the mean absolute error as the loss function, i.e. the average of the absolute values of the deviations of the arithmetic mean of the individual predictors from all predictors, and the mean squared logarithmic error as the monitoring index of the network, i.e. the square of the mean of the logarithmic loss function.
7. The method of claim 3, wherein the test set is used to predict in a trained GRU neural network model to obtain the prediction results of hour, day, zhou Pindu, respectively.
8. An electric vehicle charge capacity demand prediction system, comprising:
the collecting module is used for collecting the charging power data after the aggregation of the electric automobiles in the cells and the attribute data of the cells;
the preprocessing module is used for preprocessing the charging power data and the cell attribute data to obtain data which are grouped according to a time sequence;
the data dividing module is used for dividing the preprocessed data into a training set, a verification set and a test set;
and the training prediction module is used for constructing a GRU neural network model, training the model and predicting the trained model by using the test data set.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the electric vehicle charge capacity demand prediction method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the electric vehicle charge capacity demand prediction method according to any one of claims 1 to 7.
CN202310170649.7A 2023-02-27 2023-02-27 Electric vehicle charging capacity demand prediction method and related device Pending CN116187562A (en)

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